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Record W1992349408 · doi:10.3390/en8053503

Sustainability Assessment of the Agricultural and Energy Systems of Senegal

2015· article· en· W1992349408 on OpenAlexaff
Kyrke Gaudreau, Robert Gibson

Bibliographic record

VenueEnergies · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of WaterlooUniversity of Northern British Columbia
Fundersnot available
KeywordsSustainabilityScope (computer science)Context (archaeology)AgricultureJudgementEnvironmental economicsBusinessSuiteEnvironmental resource managementEnvironmental planningRisk analysis (engineering)Computer scienceEconomicsPolitical scienceEnvironmental science

Abstract

fetched live from OpenAlex

To improve decision-making, sustainability-based approaches to assessment of options and undertakings demand that we move beyond narrowly defined considerations to address the full suite of requirements for progress towards sustainability. This paper reports on a sustainability assessment exercise that originally focused on burning agricultural residues, primarily peanut shells, for cooking applications in Senegal. The scope of assessment had to be expanded to address the agricultural and energy systems of Senegal, when closer examination revealed a complex set of energy and agricultural system interactions that could undermine the anticipated positive effects of initiatives centred primarily on peanut residue cookstoves. The case highlights the need to be open to expanding the scope of assessment to address underlying and/or unexpected issues that cannot be addressed appropriately at the project scale. In particular, the case illustrates how the assessment of an energy system may serve as an entry point into a deeper exploration of the context in which the energy system is embedded. The analysis also illustrates a situation in which different paths that may be followed, each with its own degree of uncertainty, path dependence, feasibility, fairness, cultural sensitivity, trade-off acceptability and possibilities for public judgement of overall desirability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.253
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2015
Admission routes1
Has abstractyes

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